[113.02] Cramer-Rao Bound on Object Estimation from HST Imagery

When creating an enhanced image or estimating an object's
parameters, it is important to establish the error associated
with the processing. The Cramer-Rao bound is a tool to
calculate the error. It gives the minimum variance that can be
achieved by any estimation technique; when the technique is
maximum likelihood, the estimator asymptotically approaches the
bound, as the number of observations becomes large. We calculate
the bound for HST WF/PC imagery and show how it can be used as an
adjoint to any enhancement or estimation algorithm. The bound is
sensitive to the accuracy of the point-spread function (PSF). We
show results for an improved estimation of the PSF.